aiqbench analysis
This research addresses a critical gap in robotic manipulation by enabling policies trained on static objects to adapt dynamically to moving targets using counterfactual action augmentation. By factorizing the task into motion prediction and relative pose preservation, SIDO successfully bridges the divide between controlled lab environments and real-world scenarios involving drifting parts or swaying produce. The method demonstrates robust performance across both simulation and physical deployments without degrading accuracy on static tasks.
Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation
arXiv:2607.27890v1 Announce Type: new Abstract: Visuomotor policies have advanced on manipulation tasks where the target object stays static during execution, but real deployments break this assumption: parts drift on conveyors and fruits sway in the wind. We introduce Static In, Dynamic Out (SIDO), a counterfactual action augmentation that enables a policy trained only on static object demonstrations to adapt to unseen object motion at test time. Our key idea is to factorize moving object manipulation into two sub-problems: predicting where the object will be, and reaching that predicted pose. SIDO displaces the object to a counterfactual future position and morphs the demonstrated action chunk to preserve the hand-object relative pose, yielding a goal-conditioned policy. At deployment an object pose predictor supplies the future position. Across three simulated tasks (Mug, Square, Stack) under five object motion patterns and two real-world tasks (Gantry, Peachtree), SIDO improves moving object success over the baselines while preserving static object performance. Project website: https://sido-staticindynamicout.github.io/.
Abstract:Visuomotor policies have advanced on manipulation tasks where the target object stays static during execution, but real deployments break this assumption: parts drift on conveyors and fruits sway in the wind. We introduce Static In, Dynamic Out (SIDO), a counterfactual action augmentation that enables a policy trained only on static object demonstrations to adapt to unseen object motion at test time. Our key idea is to factorize moving object manipulation into two sub-problems: predicting where the object will be, and reaching that predicted pose. SIDO displaces the object to a counterfactual future position and morphs the demonstrated action chunk to preserve the hand-object relative pose, yielding a goal-conditioned policy. At deployment an object pose predictor supplies the future position. Across three simulated tasks (Mug, Square, Stack) under five object motion patterns and two real-world tasks (Gantry, Peachtree), SIDO improves moving object success over the baselines while preserving static object performance. Project website: this https URL.
Robotics (cs.RO)
arXiv:2607.27890 [cs.RO]
(or arXiv:2607.27890v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.27890 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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